Able C, Hampton C, Baydush A, Bright M
Wake Forest School of Medicine, Winston-Salem, NC 27157.
Med Phys. 2012 Jun;39(6Part13):3750. doi: 10.1118/1.4735265.
MLC failure increases accelerator downtime and negatively affects the clinic treatment delivery schedule. This study investigates the use of Statistical Process Control (SPC), a modern quality control methodology, to retrospectively evaluate MLC performance data thereby predicting the impending failure of individual MLC leaves.
SPC, a methodology which detects exceptional variability in a process, was used to analyze MLC leaf velocity data. A MLC velocity test is performed weekly on all leaves during morning QA. The leaves sweep 15 cm across the radiation field with the gantry pointing down. The leaf speed is analyzed from the generated dynalog file using quality assurance software. MLC leaf speeds in which a known motor failure occurred (8) and those in which no motor replacement was performed (11) were retrospectively evaluated for a 71 week period. SPC individual and moving range (I/MR) charts were used in the analysis. The I/MR chart limits were calculated using the first twenty weeks of data and set at 3 standard deviations from the mean.
The MLCs in which a motor failure occurred followed two general trends: (a) no data indicating a change in leaf speed prior to failure (5 of 8) and (b) a series of data points exceeding the limit prior to motor failure (3 of 8). I/MR charts for a high percentage (8 of 11) of the non-replaced MLC motors indicated that only a single point exceeded the limit. These single point excesses were deemed false positives.
SPC analysis using MLC performance data may be helpful in detecting a significant percentage of impending failures of MLC motors. The ability to detect MLC failure may depend on the method of failure (i.e. gradual or catastrophic). Further study is needed to determine if increasing the sampling frequency could increase reliability. Project was support by a grant from Varian Medical Systems, Inc.
多叶准直器（MLC）故障会增加加速器停机时间，并对临床治疗计划产生负面影响。本研究调查了使用统计过程控制（SPC）这一现代质量控制方法，以回顾性评估MLC性能数据，从而预测单个MLC叶片即将出现的故障。
SPC是一种检测过程中异常变异的方法，用于分析MLC叶片速度数据。在早晨质量保证期间，每周对所有叶片进行一次MLC速度测试。叶片在机架向下的情况下，在辐射野中扫描15厘米。使用质量保证软件从生成的动态日志文件中分析叶片速度。对已知发生电机故障的8个MLC叶片速度以及未进行电机更换的11个MLC叶片速度进行了为期71周的回顾性评估。分析中使用了SPC个体和移动极差（I/MR）控制图。I/MR控制图的界限使用前二十周的数据计算得出，并设定为距平均值3个标准差。
发生电机故障的MLC呈现出两种总体趋势：（a）在故障前没有数据表明叶片速度发生变化（8个中有5个）；（b）在电机故障前有一系列数据点超过界限（8个中有3个）。对于高比例（11个中有8个）未更换电机的MLC，其I/MR控制图表明只有单个点超过界限。这些单点超标被视为误报。
使用MLC性能数据进行SPC分析可能有助于检测出相当比例的即将发生的MLC电机故障。检测MLC故障的能力可能取决于故障方式（即渐进式或灾难性）。需要进一步研究以确定增加采样频率是否可以提高可靠性。本项目由瓦里安医疗系统公司的一项资助支持。
